2023
DOI: 10.3390/molecules28062803
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Protected Geographical Indication Discrimination of Zhejiang and Non-Zhejiang Ophiopogonis japonicus by Near-Infrared (NIR) Spectroscopy Combined with Chemometrics: The Influence of Different Stoichiometric and Spectrogram Pretreatment Methods

Abstract: This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas (‘non-Zhejiang’). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross val… Show more

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Cited by 4 publications
(2 citation statements)
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“…The Savitzky–Golay smoothing filter (S–G) is capable of mitigating the impact of interfering factors, such as the signal-to-noise ratio and dark current, thereby enhancing the accuracy and reliability of the data analysis [ 44 , 45 ]. The standard normal variable (SNV) is primarily employed to eliminate the effects of particle size, surface scattering and light path variation on diffuse reflection spectra [ 18 , 46 ]. Therefore, the effective spectrum was preprocessed using the S–G, SNV and S–G + SNV approaches, which reduced interference before modeling and effectively improved the prediction accuracy of the model, as shown in Figure 6 .…”
Section: Methodsmentioning
confidence: 99%
“…The Savitzky–Golay smoothing filter (S–G) is capable of mitigating the impact of interfering factors, such as the signal-to-noise ratio and dark current, thereby enhancing the accuracy and reliability of the data analysis [ 44 , 45 ]. The standard normal variable (SNV) is primarily employed to eliminate the effects of particle size, surface scattering and light path variation on diffuse reflection spectra [ 18 , 46 ]. Therefore, the effective spectrum was preprocessed using the S–G, SNV and S–G + SNV approaches, which reduced interference before modeling and effectively improved the prediction accuracy of the model, as shown in Figure 6 .…”
Section: Methodsmentioning
confidence: 99%
“…However, the NIR spectrum is characterized by complex and overlapping peaks and useless information from background and noise, resulting in difficulty in clearly distinguishing the specific spectral range corresponding to the biochemical substance. To enhance model accuracy, researchers commonly employ spectral pretreatment methods such as standard normal variable (SNV) transformation, Savitzky-Golay (SG) smoothing, first derivative (1D), second derivative (2D), and multivariate scattering correction (MSC) to remove noise interference, linearity correction, and spectral fitting [16]. Wavelength selection methods are crucial for feature extraction.…”
Section: Introductionmentioning
confidence: 99%